Member of Technical Staff (Machine Learning Engineer, Search)
Perplexity·London, England, United Kingdom | Belgrade, Serbia | Berlin, Berlin, Germany·posted 508d ago · last seen 23m agoTrack this application
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Get Started FreeAbout interviewing at Perplexity
One of the few AI startups with a fully published interview guide (perplexity.ai/hub/careers/interview-guide): online application (response within two weeks) → recruiter phone screen → a technical screen that for engineers is 'usually a standard technical programming interview' → a quickly-scheduled onsite of 4–5 interviews including a hiring-manager deep dive on past work and experience anecdotes → a final interview with a Perplexity founder or leader → decision within a week of the onsite. Coding leans Python and mixes LeetCode medium–hard with practical search-flavored tasks (ranking/filtering, concurrency, data handling); system design is AI-native (RAG pipelines, retrieval at scale, LLM serving cost/latency). Applicants are judged 'solely on merit and potential impact' and must show 'frontier knowledge and excellence in at least one area'; roles are broad by default with team matching happening during the onsite, every role — managers included — is hands-on, and building AI products isn't expected but fluency in using AI tools is required. In-person 4 days/week near an office; remote is case-by-case.
Read the full Perplexity interview process →Description
Perplexity is seeking an experienced Machine Learning Engineer to help build the next generation of advanced search technologies, with a focus on retrieval and ranking.
Responsibilities
Relentlessly push search quality forward—through models, data, tools, or any other leverage available
Architect and build core components of our search platform and model stack
Train and evaluate retrieval, ranking and classification models, including LLMs
Deploy models - from boosting to LLMs - in a scalable and performant way
Build and optimize RAG pipelines for grounding and answer generation
Collaborate with Data, AI, Infrastructure and Product teams to ensure fast and high quality delivery
Qualifications
Deep understanding of search and retrieval systems, including quality evaluation principles and metrics
Proven track record with large-scale search or recommender systems
Self-driven, with a strong sense of ownership and execution
Minimum of 5 years of working on search or recsys-related projects
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